Dependence plot for SHAP values of CCI.

BackgroundTo construct several prediction models for the risk of stroke in coronary artery disease (CAD) patients receiving coronary revascularization based on machine learning methods.MethodsIn total, 5757 CAD patients receiving coronary revascularization admitted to ICU in Medical Information Mart for Intensive Care IV (MIMIC-IV) were included in this cohort study. All the data were randomly split into the training set (n = 4029) and testing set (n = 1728) at 7:3. Pearson correlation analysis and least absolute shrinkage and selection operator (LASSO) regression model were applied for feature screening. Variables with Pearson correlation coefficientResultsThe Catboost model presented the best predictive performance with the AUC of 0.831 (95%CI: 0.811–0.851) in the training set, and 0.760 (95%CI: 0.722–0.798) in the testing set. The AUC of the logistic regression model was 0.789 (95%CI: 0.764–0.814) in the training set and 0.731 (95%CI: 0.686–0.776) in the testing set. The results of Delong test revealed that the predictive value of the Catboost model was significantly higher than the logistic regression model (PConclusionThe Catboost model was the optimal model for predicting the risk of stroke in CAD patients receiving coronary revascularization, which might provide a tool to quickly identify CAD patients who were at high risk of postoperative stroke.</div

Summary plot for SHAP values of features in the Catboost model.

Summary plot for SHAP values of features in the Catboost model.</p

The changes of Coefficients with Lambda in the Lasso regression.

The changes of Coefficients with Lambda in the Lasso regression.</p

Parameter settings for 8 machine learning models.

BackgroundTo construct several prediction models for the risk of stroke in coronary artery disease (CAD) patients receiving coronary revascularization based on machine learning methods.MethodsIn total, 5757 CAD patients receiving coronary revascularization admitted to ICU in Medical Information Mart for Intensive Care IV (MIMIC-IV) were included in this cohort study. All the data were randomly split into the training set (n = 4029) and testing set (n = 1728) at 7:3. Pearson correlation analysis and least absolute shrinkage and selection operator (LASSO) regression model were applied for feature screening. Variables with Pearson correlation coefficientResultsThe Catboost model presented the best predictive performance with the AUC of 0.831 (95%CI: 0.811–0.851) in the training set, and 0.760 (95%CI: 0.722–0.798) in the testing set. The AUC of the logistic regression model was 0.789 (95%CI: 0.764–0.814) in the training set and 0.731 (95%CI: 0.686–0.776) in the testing set. The results of Delong test revealed that the predictive value of the Catboost model was significantly higher than the logistic regression model (PConclusionThe Catboost model was the optimal model for predicting the risk of stroke in CAD patients receiving coronary revascularization, which might provide a tool to quickly identify CAD patients who were at high risk of postoperative stroke.</div

Sensitivity analysis of data before and after manipulation.

Sensitivity analysis of data before and after manipulation.</p

Force plot for SHAP values of features in the Catboost model.

Force plot for SHAP values of features in the Catboost model.</p

Comparisons of the characteristics of participants with and without postoperative stroke in the training set.

Comparisons of the characteristics of participants with and without postoperative stroke in the training set.</p

Absolut summary plot showing the importance of each feature in the Catboost model.

Absolut summary plot showing the importance of each feature in the Catboost model.</p

The ROC curves of machine learning models in the training set.

The ROC curves of machine learning models in the training set.</p

The changes of MSE with Lambda in the Lasso regression.

The changes of MSE with Lambda in the Lasso regression.</p